Associate Principal Engineer

Requirements

• Experience : 9+years
• Strong experience in data architecture, data engineering, or enterprise data platforms, with substantial hands-on experience in Snowflake.
• Strong expertise in Technology Architecture and Data Architecture, including solution design, data modeling, integration, and implementation of scalable enterprise platforms.
• Strong hands-on experience with Snowflake security, data governance frameworks, access management, and compliance controls.
• Proficiency in Snowflake Row Access Policies (RAP), Role-Based Access Control (RBAC), role hierarchies, least-privilege permissions, dynamic data masking, and tag-based masking policies.
• Experience enforcing access controls at database, schema, object, row, and column levels.
• Strong understanding of data ownership, stewardship, classification, metadata management, data lineage, data quality, and audit requirements.
• Strong SQL skills, including complex queries, stored procedures, incremental processing, and query performance tuning.
• Strong Python programming skills for data processing, API integration, automation, and pipeline development.
• Experience with PostgreSQL and enterprise database architecture, including data modeling, integration, and performance optimization.
• Hands-on experience with Streamlit application development and Snowflake Cortex capabilities, including Cortex Analyst, Cortex Search, Cortex Agents, and semantic modeling.
• Ability to evaluate AI-generated responses and implement appropriate security and governance controls for AI applications.
• Experience building and managing data ingestion and transformation pipelines using Azure Data Factory, AWS Glue, Python, or equivalent tools.
• Strong understanding of full loads, incremental loads, CDC, data validation, error handling, and pipeline monitoring.
• Familiarity with AWS services such as S3 and IAM, including secure integration with Snowflake.
• Experience using Git, Azure DevOps, and CI/CD pipelines to version, review, test, and deploy database objects, security policies, and application changes across environments.
• Knowledge of data privacy, PII protection, access reviews, audit evidence, data retention, and regulatory compliance requirements.
• Strong architectural leadership skills, with the ability to define standards, evaluate design decisions, and guide technical teams toward secure and maintainable solutions.
• Excellent communication and stakeholder management skills, with the ability to explain complex architecture, governance, security, and AI concepts to technical and business audiences.
• Strong analytical and problem-solving abilities, including the capacity to clarify ambiguous requirements, identify risks, and recommend practical solutions.
• Ability to manage multiple initiatives, dependencies, and timelines while collaborating with distributed teams.
• Experience mentoring engineers and driving technical excellence across data architecture and engineering teams.
• PL/SQL knowledge is desirable.
• Relevant Snowflake, cloud, or data architecture certifications are preferred.
Responsibilities

• Define, implement, and maintain enterprise data governance standards covering data ownership, stewardship, classification, metadata, lineage, data quality, and compliance.
• Design and implement secure, scalable Snowflake data architectures aligned with enterprise architecture and security standards.
• Develop and maintain Snowflake Row Access Policies (RAP) to control data visibility based on region, market, business unit, and user entitlements.
• Implement Role-Based Access Control (RBAC), role hierarchies, least-privilege access, and database-level permissions.
• Protect sensitive and personally identifiable information (PII) using dynamic data masking, tag-based masking policies, and column-level security.
• Enforce access controls across database, schema, object, row, and column levels.
• Establish access reviews, audit monitoring, secure authentication, and integration practices to meet organizational privacy, retention, and compliance requirements.
• Design scalable data models and provide architectural guidance on data integration, SQL optimization, and Snowflake performance tuning.
• Develop and optimize SQL and Python solutions for data processing, automation, integration, and incremental data processing.
• Build and maintain reliable data ingestion and transformation pipelines using Python, Azure Data Factory, AWS Glue, or equivalent technologies.
• Implement full-load, incremental-load, and Change Data Capture (CDC) processes with appropriate data validation, error handling, and pipeline monitoring.
• Develop interactive Streamlit applications to enable analytics and conversational access to enterprise data.
• Implement Snowflake Cortex Analyst, Cortex Search, Cortex Agents, and semantic models to support natural-language queries, information retrieval, and AI-driven analytics.
• Evaluate AI response accuracy and ensure appropriate data governance, security, and access controls within AI-enabled applications.
• Analyze Snowflake query profiles, optimize warehouse utilization, and balance performance with cost efficiency.
• Automate the deployment of database objects, security policies, and application changes using Git, Azure DevOps, and CI/CD pipelines.
• Integrate Snowflake securely with cloud services, including AWS S3 and IAM.
• Collaborate with business stakeholders, data owners, security teams, governance teams, and engineering teams to establish and enforce architecture standards.
• Lead solution design, assess technical risks, resolve architectural challenges, and guide implementation decisions across enterprise data initiatives.
• Mentor engineers, review technical designs, and promote secure, scalable, maintainable, and compliant data solutions.
• Manage multiple initiatives, dependencies, and delivery timelines while ensuring alignment with business and technology objectives.

Department: Engineering
Function: Information Technology
Experience Level: Not Applicable